File size: 2,559 Bytes
1b5ae91 40121f1 1b5ae91 0cfa9c7 1b5ae91 07d2745 1b5ae91 be1bb03 1b5ae91 4708344 1b5ae91 2900a17 1b5ae91 40121f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
license: cc-by-nc-4.0
language:
- fa
library_name: transformers
tags:
- text-generation-inference
inference: false
pipeline_tag: text-generation
---
# PersianLLaMA: Towards Building First Persian Large Language Model
<img src="https://huggingface.co/ViraIntelligentDataMining/PersianLLaMA-2-13B/resolve/main/persianllama.png" alt="PersianLLaMA" width=400/>
## 🌟 Introduction
Welcome to the home of PersianLLaMA, the pioneering large language model for the Persian language. With 13 billion parameters, this model is trained on Persian Wikipedia corpus and designed to excel in multiple NLP tasks, setting a new benchmark for Persian language understanding and generation.
## 🛠 Model Description
PersianLLaMA is not just a model but a comprehensive tool for:
- 📝 **Text Generation**: Crafting coherent and contextually appropriate text.
- 🎯 **Instruct Tuning**: Executing tasks based on detailed instructions, ideal for scenarios where the model needs to adhere to specific guidelines or produce outputs tailored to particular requirements.
- ❓ **Question Answering**: Providing accurate answers to Persian queries.
- 📊 **Text Summarization**: Condensing Persian texts into precise summaries.
This model has been collaboratively developed by a team of experts, including Mohammad Amin Abbasi, Arash Ghafouri, Mahdi Firouzmandi, Hassan Naderi, Behrouz Minaei Bidgoli.
## 🚀 Quick Start
To integrate PersianLLaMA into your project, follow these steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "ViraIntelligentDataMining/PersianLLaMA-13B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
prompt = "این متن به فارسی است"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs["input_ids"])
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## 📈 Evaluation and Benchmarks
PersianLLaMA demonstrates superior performance over existing models, with robust evaluation metrics that highlight its capabilities in natural language understanding and generation.
## 📜 Citing PersianLLaMA
If you find PersianLLaMA useful in your research, please consider citing:
```bibtex
@article{abbasi2023persianllama,
title={PersianLLaMA: Towards Building First Persian Large Language Model},
author={Abbasi, Mohammad Amin and others},
journal={https://arxiv.org/abs/2312.15713},
year={2023}
}
```
## 📄 License
PersianLLaMA is open-sourced under the CC BY-NC 4.0 license. |